From c419f8ce962a2951ba6db7dc50f7e6ac4a2bff83 Mon Sep 17 00:00:00 2001 From: Michael Date: Fri, 22 Feb 2019 14:57:44 +0000 Subject: [PATCH] Update NEWS and links in README --- NEWS.md | 6 ++++++ README.md | 22 +++++++++++----------- 2 files changed, 17 insertions(+), 11 deletions(-) diff --git a/NEWS.md b/NEWS.md index 2c3e843d..2228d269 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,12 @@ ## Upcoming changes - Update of PSD decomposition related code to Julia v1.0 +## Version 0.4 (22. February 2019) +- The optional arguments `settings`, `x0`, `y0`, `s0` are now keyword arguments (breaks backward compatibility), see d0f22aab +- Some smaller bug fixes and function renaming +- Documentation via Documenter.jl + + ## Version 0.3.1 (2. February 2019) - Support warm starting of primal and dual variables in MOI / JuMP interface - Fix a bug when printing the header diff --git a/README.md b/README.md index 23cc05e0..4bfccde7 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,6 @@

Features • Installation • - Documentation • News • Citing • Contributing @@ -28,21 +27,22 @@ This is a Julia implementation of the _Conic operator splitting method_ (COSMO) solver. It can solve large convex conic optimization problems of the following form:

- +

-with decision variables `x ϵ R^n`, `s ϵ R^m` and data matrices `P=P'>=0`, `q ϵ R^n`, `A ϵ R^(m×n)`, and `b ϵ R^m`. The convex set `C` is a composition of convex sets and cones. +with decision variables `x ϵ R^n`, `s ϵ R^m` and data matrices `P=P'>=0`, `q ϵ R^n`, `A ϵ R^(m×n)`, and `b ϵ R^m`. The convex set `K` is a composition of convex sets and cones. -__For more information check the [COSMO.jl Documentation](https://oxfordcontrol.github.io/COSMO.jl/stable).__ +__For more information check the [COSMO.jl Documentation](https://oxfordcontrol.github.io/COSMO.jl/dev).__ ## Features -By default COSMO supports the zero cone, the non-negative orthant, second order cones and positive semidefinite cones. COSMO allows you to: -- solve LPs, QPs, SOCPs and SDPs -- solve semidefinite programs with quadratic objective functions directly -- detect infeasible problems without a homogeneous self-dual embedding of the problem -- describe your optimisation problem using [JuMP](https://github.com/JuliaOpt/JuMP.jl) (COSMO requires JuMP v0.19) -- use chordal decomposition techniques to decompose chordally structured SDPs -- define your own convex sets for constraints + +* __Versatile__: COSMO solves linear programs, quadratic programs, second-order cone programs and semidefinite programs +* __Quad SDPs__: Positive semidefinite programs with quadratic objective functions are natively supported +* __Infeasibility detection__: Infeasible problems are detected without a homogeneous self-dual embedding of the problem +* __JuMP support__: COSMO supports MathOptInterface and JuMP `v0.19`, which allows you to describe your problem in JuMP +* __Chordal decomposition__: COSMO tries to decompose large structured PSD constraints using chordal decomposition techniques. This often results in a significant speedup compared to the original problem. +* __Warm starting__: COSMO supports warm starting of the decision variables +* __Open Source__: Our code is free to use and distributed under the Apache 2.0 Licence ## Installation - `COSMO` can be added via the Julia package manager (type `]`): `pkg> add COSMO`